bims-lorfki Biomed News
on Long non-coding RNA functions in the kidney
Issue of 2021‒07‒25
five papers selected by
Nikita Dewani
Max Delbrück Centre for Molecular Medicine

  1. Front Mol Biosci. 2021 ;8 697962
      Long non-coding RNA (lncRNA) is a kind of RNA that possesses longer than 200 nucleotides and lacks protein coding function. It was recognized as a junk sequence for a long time. Recent studies have found that lncRNAs are actively functioning in almost every aspect of cell biology and involved in a variety of biological functions. LncRNAs are closely related to a variety of human diseases, especially tumors. Recently, lncRNAs are being increasingly reported in renal cancer. In our study, we identified the expression of lncRNA LINC00944 is significantly elevated in renal cell carcinoma (RCC) tissues and cell lines and high LINC00944 expression is significantly correlated with the tumor stage and prognosis of RCC. The knockdown of LINC00944 by CRISPR/dCas9-KRAB in higher expressing 786-O and 769-P RCC cells could significantly decrease proliferation and migration and also promote phosphorylation of Akt compared with the control group. Our study is the first to report the function of lncRNA LINC00944 in RCC. And we provide clinicopathological and experimental evidence that lncRNA LINC00944 acts as an oncogene in RCC, suggesting that targeting lncRNA LINC00944 expression might be a promising therapeutic strategy for the treatment of RCC.
    Keywords:  Akt; LINC00944; RCC; phosphorylation; tumorigenesis
  2. Front Mol Biosci. 2021 ;8 682471
      Background: N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) have been proven to be involving in regulating tumorigenesis, invasion, and metastasis for a variety of tumors. The present study aimed to screen lncRNAs with m6A modification and investigate their biological signatures and prognostic values in kidney renal clear cell carcinoma (KIRC). Materials and Methods: lncRNA-seq, miRNA-seq, and mRNA-seq profiles of KIRC samples and the clinical characteristics of corresponding patients were downloaded from The Cancer Genome Atlas (TCGA). The R package "edgeR" was utilized to perform differentially expressed analysis on these profiles to gain DElncRNAs, DEmiRNAs, and DEmRNAs, respectively. The results of intersection of DElncRNAs and m6A-modified genes were analyzed by the weighted gene co-expression network analysis (WGCNA) to screen hub m6A-lncRNAs. Then, WGCNA was also used to construct an lncRNA-miRNA-mRNA (ceRNA) network. The Cox regression analysis was conducted on hub m6A-lncRNAs to construct the m6A-lncRNAs prognostic index (m6AlRsPI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of m6AlRsPI. The m6AlRsPI model was tested by internal and external cohorts. The molecular signatures and prognosis for hub m6A-lncRNAs and m6AlRsPI were analyzed. The expression level of hub m6A-lncRNAs in KIRC cell lines were quantified by qRT-PCR. Results: A total of 21 hub m6A-lncRNAs associated with tumor metastasis were identified in the light of WGCNA. The ceRNA network for 21 hub m6A-lncRNAs was developed. The Cox regression analysis was performed on the 21 hub m6A-lncRNAs, screening two m6A-lncRNAs regarded as independent prognostic risk factors. The m6AlRsPI was established based on the two m6A-lncRNAs as follows: (0.0006066 × expression level of LINC01820) + (0.0020769 × expression level of LINC02257). The cutoff of m6AlRsPI was 0.96. KM survival analysis for m6AlRsPI showed that the high m6AlRsPI group could contribute to higher mortality. The area under ROC curve for m6AlRsPI for predicting 3- and 5-year survival was 0.760 and 0.677, respectively, and the m6AlRsPI was also tested. The mutation and epithelial-mesenchymal transition (EMT) analysis for m6AlRsPI showed that the high m6AIRsPI group had more samples with gene mutation and had more likely caused EMT. Finally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for mRNAs interacted with the two m6A-lncRNAs, showing they were involved in the process of RNA splicing and regulation of the mRNA surveillance pathway. qRT-PCR analysis showed that the two m6A-lncRNAs were upregulated in KIRC. Conclusion: In the present study, hub m6A-lncRNAs were determined associated with metastasis in KIRC, and the ceRNA network demonstrated the potential carcinogenic regulatory pathway. Two m6A-lncRNAs associated with the overall survival were screened and m6AlRsPI was constructed and validated. Finally, the molecular signatures for m6AlRsPI and the two m6A-lncRNAs were analyzed to investigate the potential modulated processes in KIRC.
    Keywords:  N6-methyladenosine; WGCNA; kidney renal clear cell carcinoma; long noncoding RNA; prognostic index
  3. Oncol Rep. 2021 Sep;pii: 187. [Epub ahead of print]46(3):
      Renal cell carcinoma (RCC) is a major healthcare burden globally. Tumor‑derived extracellular vesicles (EVs) contribute to the formation of a pro‑metastatic microenvironment. In the present study, we explored the role and mechanism of RCC cell 786‑O‑derived EVs (786‑O‑EVs) in RCC. First, 786‑O‑EVs were extracted and identified, and EV internalization of RCC cells was observed. RCC cell malignant behaviors and long noncoding RNA (lncRNA) metastasis‑associated lung adenocarcinoma transcript 1 (MALAT1) expression patterns were detected before and after 786‑O‑EV treatment. MALAT1 was intervened to evaluate RCC cell behaviors. The downstream mechanism involving MALAT1 was predicted. In addition, the relationship among MALAT1, transcription factor CP2 like 1 (TFCP2L1) and ETS proto‑oncogene 1, transcription factor (ETS1) was analyzed. TFCP2L1 expression patterns were measured after 786‑O‑EV exposure. Tumor xenograft formation assay and lung metastasis model were adopted to verify the role of 786‑O‑EVs in vivo in RCC. It was found that 786‑O‑EVs could be internalized by RCC cells. 786‑O‑EVs promoted RCC cell malignant behaviors, accompanied by elevated MALAT1 expression levels. The 786‑O‑EVs with MALAT1 knockdown attenuated the promotive effect of sole 786‑O‑EVs on RCC cells. MALAT1 located ETS1 in the TFCP2L1 promoter and negatively regulated TFCP2L1, and ETS1 protein could specifically bind to MALAT1. 786‑O‑EVs enhanced the binding of ETS1 and the TFCP2L1 promoter and decreased TFCP2L1 expression. In vivo, 786‑O‑EVs promoted tumor growth and RCC lung metastasis, which was suppressed following inhibition of MALAT1. Our findings indicated that 786‑O‑EVs promoted RCC invasion and metastasis by transporting MALAT1 to promote the binding of transcription factor ETS1 and TFCP2L1 promoter.
    Keywords:  ETS1; TFCP2L1; extracellular vesicles; invasion; lncRNA MALAT1; migration; renal cell carcinoma
  4. Transl Androl Urol. 2021 Jun;10(6): 2478-2492
      Background: The immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC.Methods: The raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score.
    Results: There were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC. We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine.
    Conclusions: Our study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.
    Keywords:  Immune-related long non-coding RNAs (irlncRNAs); clear cell renal cell carcinoma (ccRCC); prognostic biomarkers
  5. Genomics Proteomics Bioinformatics. 2021 Jul 17. pii: S1672-0229(21)00155-8. [Epub ahead of print]
      The development of new biomarkers or therapeutic targets for cancer immunotherapies requires deep understanding of T cells. To date, the complete landscape and systematic characterization of long noncoding RNAs (lncRNAs) in T cells in cancer immunity are lacking. Here, by systematically analyzing full-length single-cell RNA sequencing (scRNA-seq) data of more than 20,000 libraries of T cells across three cancer types, we provide the first comprehensive catalog and the functional repertoires of lncRNAs in human T cells. Specifically, we developed a custom pipeline for de novo transcriptome assembly and obtained a novel lncRNA catalog containing 9433 genes. This increased the number of current human lncRNA catalog by 16% and nearly doubled the number of lncRNAs expressed in T cells. We found that a portion of expressed genes in single T cells were lncRNAs which had been overlooked by the majority of previous studies. Based on metacell maps constructed by the MetaCell algorithm that partitions scRNA-seq datasets into disjointed and homogenous groups of cells (metacells), 154 signature lncRNAs were identified. They associated with effector, exhausted, and regulatory T cell states. 84 of them were functionally annotated based on the co-expression network, indicating that lncRNAs might broadly participate in the regulation of T cell functions. Our findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.
    Keywords:  Functional annotation; Immune regulation; Long non-coding RNA; Metacell; Transcriptome assembly